The relaunch of SuperLocalMemory, rebuilt from the ground up and backed by four arXiv papers. One local-first memory brain for Claude Code, Codex, Cursor, ChatGPT and any MCP or CLI agent. It never bluffs: recall says supported, related or no answer, checked by Laya on your Mac or Jev online. Find pictures and PDFs by describing them, with Google’s EmbeddingGemma 2. Governed, mesh-connected, AGPL-3.0. Built by Qualixar, the AI Reliability Engineering lab behind 10 open-source products.
Hi Product Hunt! This is a relaunch of SuperLocalMemory, and it is a real rebuild, not a new coat of paint.
The first versions could save and retrieve context. The harder failure showed up later: a retrieval layer usually returns the closest thing it has, even when that thing does not answer the agent's question. Once several agents shared durable state, I also needed to know when a fact was true, which agent wrote it, who could read it, and whether deleting it really removed the derived copies.
So I rebuilt it around those failure modes:
- It says "supported", "related" or "no answer" instead of guessing, with an optional Answer Check.
- One memory shared by Claude Code, Codex, Cursor and any MCP agent, over MCP or the CLI.
- Optional local picture and PDF memory.
- Time-aware recall, and every fact keeps who wrote it and when.
- Profiles, scopes, roles, an audit trail and verified erasure.
The core memory path runs locally. Everything is open source under AGPL-3.0.
I'd especially value feedback from people running more than one coding agent on the same project: where does shared memory help, and where does it get dangerous?
GitHub: https://github.com/qualixar/supe...
Teaser: https://youtu.be/X11YRkc0y9Q
Website: https://superlocalmemory.com
Technical write-up: https://qualixar.com/research/bl...
About SuperLocalMemory on Product Hunt
“Agent memory with Laya, Jev + Google Gemma”
SuperLocalMemory launched on Product Hunt on October 15th, 2026 and earned 84 upvotes and 7 comments, placing #18 on the daily leaderboard. The relaunch of SuperLocalMemory, rebuilt from the ground up and backed by four arXiv papers. One local-first memory brain for Claude Code, Codex, Cursor, ChatGPT and any MCP or CLI agent. It never bluffs: recall says supported, related or no answer, checked by Laya on your Mac or Jev online. Find pictures and PDFs by describing them, with Google’s EmbeddingGemma 2. Governed, mesh-connected, AGPL-3.0. Built by Qualixar, the AI Reliability Engineering lab behind 10 open-source products.
On the analytics side, SuperLocalMemory competes within Open Source, Developer Tools, Artificial Intelligence and GitHub — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how SuperLocalMemory performed against the three products that launched closest to it on the same day.
Who hunted SuperLocalMemory?
SuperLocalMemory was hunted by varun pratap Bhardwaj. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of SuperLocalMemory including community comment highlights and product details, visit the product overview.
varun pratap Bhardwaj